Figure REF  shows a comparison between PromptGen and two previous CLIP-guided image generation methods, StyleCLIP [1]} and StyleGAN2-NADA [2]}.
We observe that PromptGen generates diverse and high-quality images of babies, while StyleCLIP struggles in controllability and image quality, and StyleGAN2-NADA generates baby-like adults.Since we care about distributions, none of the images in this paper are cherry- or lemon-picked. These results show that (1) locally editing the latent code (i.e., StyleCLIP) is not always an effective method for controlling generative models (e.g., not all images' latent code can be locally edited into a baby); (2) domain adaptation (i.e., StyleGAN-NADA) is not effective in seeking modes in a generative model.
Figure REF , Figure REF , and Figure REF  show how PromptGen applies to StyleNeRF [3]}, NVAE [4]}, and diffusion autoencoder (DiffAE; [5]}).
Although these generative models are all trained on FFHQ, baby images sampled from them have distinct characteristics.
Figure REF  shows the ability of PromptGen to generate cats of a particular species.
Figure REF  shows the extension (Section REF ) of our PromptGen to the class-embedding space of BigGAN [6]}, a class-conditional GAN trained on ImageNet [7]}.
We observe that PromptGen helps BigGAN generate images with complex text descriptions, which are out of BigGAN's training image distribution; however, the diversity seems to be limited in this extension.
